Ursino M, Artioli E, Avanzolini G, Potuto V
Department of Electronics and Automatics, University of Ancona, Italy.
Artif Intell Med. 1994 Jun;6(3):229-47. doi: 10.1016/0933-3657(94)90064-7.
In this work the possibility of building an expert system to reason on the status of post-operative cardiac patients in intensive care units is analysed. The long-term knowledge consists of causal network which describes the main relationships between hemodynamic and metabolic quantities involved in the evolution after cardiac surgery. The inference engine uses an original hybrid formalism, which integrates numerical simulation and qualitative methods. If available, the numerical values of quantities and their exact mathematical relationships are employed; otherwise, the inference engine reasons by using a discrete qualitative representation of quantities. Simulations performed using real data indicate that integration of quantitative and qualitative methods reduces the number of diagnostic scenarios compatible with patient data, and constitutes a valid tool for reasoning about physiological disorders in terms of deep causal knowledge.
在这项工作中，分析了构建一个专家系统以对重症监护病房中心脏术后患者的状况进行推理的可能性。长期知识由因果网络组成，该网络描述了心脏手术后演变过程中涉及的血流动力学和代谢量之间的主要关系。推理引擎使用一种原始的混合形式主义，它整合了数值模拟和定性方法。如果有可用的量的数值及其精确的数学关系，则采用这些数值；否则，推理引擎通过使用量的离散定性表示进行推理。使用实际数据进行的模拟表明，定量和定性方法的整合减少了与患者数据兼容的诊断场景数量，并构成了一个基于深层因果知识对生理紊乱进行推理的有效工具。
